Text Classification
Transformers
Safetensors
modernbert
code
language-identification
multi-label
llm-guard
encoder
text-embeddings-inference
Instructions to use Accuknoxtechnologies/CodeLanguage-Encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Accuknoxtechnologies/CodeLanguage-Encoder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Accuknoxtechnologies/CodeLanguage-Encoder-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Accuknoxtechnologies/CodeLanguage-Encoder-v1") model = AutoModelForSequenceClassification.from_pretrained("Accuknoxtechnologies/CodeLanguage-Encoder-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 198e21f189f7d98838eabf556769ca208cf99c2b7348f26bc434aa0ed5434278
- Size of remote file:
- 5.91 kB
- SHA256:
- 62c20387c0c96b540ddc8cd836371383a00922523f8828237d0896b8c9562a56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.